Combining 3D Radiative Transfer Model and Convolutional Neural Network to Accurately Estimate Forest Canopy Cover From Very High-Resolution Satellite Images

Combining 3D Radiative Transfer Model and Convolutional Neural Network to Accurately Estimate Forest Canopy Cover From Very High-Resolution Satellite Images

Forest canopy cover (FCC) plays an important role in many ecological, hydrological and forestry applications. For large-scale applications, FCC is usually estimated from remotely sensed data by inverting radiative transfer models (RTMs) or using data-driven regressions. In this article, we proposed...

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Detalles Bibliográficos
Autores principales: Decai Jin, Jianbo Qi, Huaguo Huang, Linyuan Li
Formato: article
Lenguaje:EN
Publicado: IEEE 2021
Materias:
Three-dimensional (3-D) radiative transfer model
convolutional neural network (CNN)
forest canopy cover (FCC)
transfer learning
Ocean engineering
TC1501-1800
Geophysics. Cosmic physics
QC801-809
Acceso en línea:https://doaj.org/article/85399ec30deb4e0aa5a97f8662d06943
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https://doaj.org/article/85399ec30deb4e0aa5a97f8662d06943

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